As in [1]}, we use a cleaned-up and processed subset of the ShapeNet dataset [2]} that features \(N = 1400\)  car meshes that are suitable for CFD simulation. For each such mesh \(\textbf {x}_{i}\) , we run OpenFOAM [3]} to estimate the pressure field \(\textbf {y}_{i}\)  created by air travelling at 15 meters per second towards the car. We also train the MeshSDF [1]} mapping \(\textbf {P}\)  on this training dataset so that for each \(\textbf {x}_{i}\)  there is 256-dimensional latent vector \(\textbf {z}_{i}\)  such that \(\textbf {x}_{i} = \textbf {P}(\textbf {z}_{i})\) . Though many mesh and graph embedding methods were proposed recently in literature [5]}, [6]}, [7]}, [8]}, MeshSDF proved to be one of the most efficient and expressive in CFD context.
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